What are the responsibilities and job description for the Bioinformatics Scientist, Bioinformatics and Research Computing position at Whitehead Institute?
OVERALL RESPONSIBILITIES
The Bioinformatics and Research Computing Innovation Center is seeking a Bioinformatics Scientist to collaborate and consult with Whitehead scientists on biomedical research projects, analyze and interpret experimental data and provide training and support to Whitehead scientists in the use of and development of Linux-based biological software.
CHARACTERISTIC DUTIES
- Collaborate with and support Whitehead scientists using public or custom software in the areas of short-read sequencing, genomics, gene expression, data mining, protein analysis, statistics, and other high-throughput analyses.
- Analyze quantitative experiments and interpret the results in the context of the collaborator’s research interests
- Create data-driven figures to communicate complex experimental results
- Identify or develop software and tools to analyze, display, and manage biological data.
- Assist with the design and development of bioinformatics-related programming projects.
- Instruct scientists in the use of the best and latest bioinformatics tools available.
- Prepare and teach classes, courses and/or demonstrations on the use of various techniques and software tools.
- Other duties, as assigned
QUALIFICATIONS
- Ph.D. in biology or related discipline.
- A minimum of two years of bioinformatics/computational biology experience.
- Background in programming (Python and/or other languages), statistics (R/Bioconductor) and the use of genome-scale analysis tools.
- Experience with the analysis of high-throughput sequencing experiments
- Excellent interpersonal, verbal and written communication skills.
- Must be able to work well independently and with all levels of Institute researchers.
- Must work well with others in a team environment.
- Must be flexible and demonstrate good skills in instruction and training.
- Experience with protein structure visualization and prediction, machine learning, deep learning, image analysis, mass spectrometry, relational databases, or high-dimensional datasets a plus.